matlab+opencv车道线识别-EW帮帮网

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matlab+opencv车道线识别
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前言

这篇博客针对《matlab+opencv车道线识别》编写代码，代码整洁，规则，易读。学习与应用推荐首选。

文章目录

一、所需工具软件
二、使用步骤
1. 主要代码
2. 运行结果
三、在线协助

一、所需工具软件

1. matlab
2. opencv

二、使用步骤

代码如下（示例）：


function lane_detection_gui()
    % 创建主窗体
    fig = uifigure('Name', '多阶段车道线检测系统', ...
                   'Position', [left, bottom, winWidth, winHeight]);

    % 全局变量存储视频路径和预警状态
    global videoPath warningLabel;
    videoPath = '';
    
    % 创建预警标签（初始时隐藏）
    warningLabel = uilabel(fig, ...
        'Position', [50, 250, 200, 100], ...  % 调整Y位置
        'Text', '', ...
        'FontSize', 16, ...
        'FontColor', 'red', ...
        'FontWeight', 'bold', ...
        'Visible', 'off');

    % 创建显示区域 (6个)，调整Y坐标以适应更大的窗口
    axOriginal = uiaxes(fig, 'Position', [250, 440, 300, 300], 'Box', 'on'); title(axOriginal, '原始帧');
    axGray     = uiaxes(fig, 'Position', [600, 440, 300, 300], 'Box', 'on'); title(axGray, '灰度图');
    axSobel    = uiaxes(fig, 'Position', [950, 440, 300, 300], 'Box', 'on'); title(axSobel, 'Sobel 边缘');
    axMask     = uiaxes(fig, 'Position', [250, 150, 300, 300], 'Box', 'on'); title(axMask, '掩膜区域');
    axHough    = uiaxes(fig, 'Position', [600, 150, 300, 300], 'Box', 'on'); title(axHough, '霍夫直线');
    axOutput   = uiaxes(fig, 'Position', [950, 150, 300, 300], 'Box', 'on'); title(axOutput, '最终检测结果');

    % 每个按钮绑定一个处理函数，调整Y位置
    uibutton(fig, 'Position', [50, 570, 150, 30], ...
        'Text', '原始帧', ...
        'ButtonPushedFcn', @(btn, event) processVideo(axOriginal, 'original', fig));

    uibutton(fig, 'Position', [50, 530, 150, 30], ...
        'Text', '灰度图', ...
        'ButtonPushedFcn', @(btn, event) processVideo(axGray, 'gray', fig));

    uibutton(fig, 'Position', [50, 490, 150, 30], ...
        'Text', 'Sobel 边缘', ...
        'ButtonPushedFcn', @(btn, event) processVideo(axSobel, 'sobel', fig));

    uibutton(fig, 'Position', [50, 450, 150, 30], ...
        'Text', '掩膜区域', ...
        'ButtonPushedFcn', @(btn, event) processVideo(axMask, 'mask', fig));

    uibutton(fig, 'Position', [50, 410, 150, 30], ...
        'Text', '霍夫直线', ...
        'ButtonPushedFcn', @(btn, event) processVideo(axHough, 'hough', fig));



    % 只有"原始帧"按钮允许选择文件
    if strcmp(type, 'original')
        [file, path] = uigetfile({'*.mp4;*.avi', '视频文件'});
        if isequal(file, 0)
            return;
        end
        videoPath = fullfile(path, file);
    end

    if isempty(videoPath)
        uialert(fig, '请先点击"原始帧"选择视频文件。', '未选择视频');
        return;
    end

    % 读取视频
    reader = VideoReader(videoPath);
    while hasFrame(reader)
        frame = readFrame(reader);
        frame = imresize(frame, [980,980]); % 将每帧缩放到 640x480
        result = frame;
        
        % 获取图像中心位置（假设车辆在图像中心）
        [rows, cols, ~] = size(frame);
        centerX = cols / 2;
        centerY = rows * 0.8;  % 假设车辆位置在图像下方80%处
        
        % 重置警告状态
        warningLabel.Visible = 'off';

        switch type
            case 'original'
                result = frame;

            case 'gray'
                result = rgb2gray(frame);

            case 'sobel'
                gray = rgb2gray(frame);
                thresh = 0.10;
                result = edge(gray, 'Sobel', thresh);

            case 'mask'
                gray = rgb2gray(frame);
                sobel = edge(gray, 'Sobel');
                result = doSegment(sobel);


            case 'hough'
                gray = rgb2gray(frame);
                sobel = edge(gray, 'Sobel');
                mask = doSegment(sobel);
                lines = houghLines(mask);
                avgLines = calculateLines(frame, lines);
                result = visualizeLines(frame, avgLines);
                
                % 检查是否接近车道线
                checkLaneProximity(avgLines, centerX, centerY, cols);

            case 'output'
                gray = rgb2gray(frame);
                sobel = edge(gray, 'Sobel');
                mask = doSegment(sobel);
                lines = houghLines(mask);
                avgLines = calculateLines(frame, lines);
                houghImg = visualizeLines(frame, avgLines);
                result = imadd(im2double(frame) * 0.9, im2double(houghImg));
                
                % 检查是否接近车道线
                checkLaneProximity(avgLines, centerX, centerY, cols);
        end

        imshow(result, 'Parent', ax);
        pause(0.03);  % 控制播放速度
    end
end

%% 检查是否接近车道线
function checkLaneProximity(avgLines, centerX, centerY, imgWidth)
    global warningLabel;
    
    % 定义安全距离（像素）
    safetyDistance = imgWidth * 0.15;  % 图像宽度的15%
    
    % 检查每条车道线
    for i = 1:size(avgLines, 1)
        if ~isempty(avgLines(i,:))
            x1 = avgLines(i,1); y1 = avgLines(i,2);
            x2 = avgLines(i,3); y2 = avgLines(i,4);
            
            % 计算车道线在车辆位置(y坐标)处的x值
            if y1 ~= y2  % 避免除以零
                slope = (x2 - x1) / (y2 - y1);
                laneX = x1 + (centerY - y1) * slope;
                
                % 计算与车道线的距离
                distance = abs(centerX - laneX);
                
                % 如果距离小于安全距离，显示警告
                if distance < safetyDistance
                    if laneX < centerX
                        warningText = '警告：靠近左侧车道线!';
                    else
                        warningText = '警告：靠近右侧车道线!';
                    end
                    warningLabel.Text = warningText;
                    warningLabel.Visible = 'on';
                    break;  % 只要接近一条线就显示警告
                end
            end
        end
    end
end

%% 掩膜函数
function segment = doSegment(frame)
    [rows, cols] = size(frame);
    mask = false(rows, cols);
    vertices = [200, rows;     % 左下角
                1700, rows;    % 右下角
                1550, 300;     % 右上角
                350, 300];    % 左上角
    mask = poly2mask(vertices(:,1), vertices(:,2), rows, cols);
    segment = frame & mask;
end